The advent of immune-checkpoint inhibitors (ICI) in modern oncology has significantly improved survival in several cancer settings. A subgroup of women with breast cancer (BC) has immunogenic infiltration of lymphocytes with expression of programmed death-ligand 1 (PD-L1). These patients may potentially benefit from ICI targeting the programmed death 1 (PD-1)/PD-L1 signaling axis. The use of tumor-infiltrating lymphocytes (TILs) as predictive and prognostic biomarkers has been under intense examination. Emerging data suggest that TILs are associated with response to both cytotoxic treatments and immunotherapy, particularly for patients with triple-negative BC. In this review from The International Immuno-Oncology Biomarker Working Group, we discuss (a) the biological understanding of TILs, (b) their analytical and clinical validity and efforts toward the clinical utility in BC, and (c) the current status of PD-L1 and TIL testing across different continents, including experiences from low-to-middle-income countries, incorporating also the view of a patient advocate. This information will help set the stage for future approaches to optimize the understanding and clinical utilization of TIL analysis in patients with BC.

Purpose: We studied the long-term outcomes of invasive micropapillary carcinoma (IMPCs) of the breast in relation to stromal tumor infiltrating lymphocytes (sTILs), prognostic biomarkers and clinico-pathological features.Methods: Stage I-III IMPCs treated with upfront surgery at our institution (January 2000 and December 2016) were included. Central pathology review was performed and sTILs (including zonal distribution and hot spot analysis) and tumor associated plasma cells (TAPC) were evaluated. Expression of P53, BCL2, FOXP3, and WT1, which are variably linked to breast cancer prognosis, was measured by immunohistochemistry using tissue microarrays.Time-to-event endpoints were distant recurrence free interval (DRFI) and breast cancer specific survival (BCSS). Results:We included 111 patients of whom 59% were pure IMPCs. Standard clinicopathological features were comparable between pure and non-pure IMPCs. Overall, the mean sTILs level was 20% with higher proportion of sTILs present at the invasive front. There were no significant differences between pure-and non-pure IMPCs in sTILs levels, nor in the spatial distribution of the hot spot regions or in the distribution of TAPC. Higher sTILs correlated with worse DRFI (HR=1.55; p=0.0172) and BCSS (HR=2.10; p<0.001).Conclusions: Clinico-pathological features, geographical distribution of sTILs and TAPC are similar between pure and non-pure IMPCs. Despite a high proportion of grade 3 tumors and lymph node involvement, we observed a low rate of distant recurrences and breast cancer related death in this cohort of stage I-III IMPCs treated with primary surgery. Caution in interpretation of the observed prognostic correlations is required given the very low number of events, warranting validation in other cohorts.

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The clinical significance of the tumor‐immune interaction in breast cancer is now established, and tumor‐infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple‐negative (estrogen receptor, progesterone receptor, and HER2‐negative) breast cancer and HER2‐positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state‐of‐the‐art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false‐positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in‐depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple‐negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.